Public lectures

Date: October 16, 2020

Time: 5:00PM - 6:00PM

Location: Zoom

Speaker: Richard Baraniuk, Rice University

Title: Going Off the Deep End with Deep Learning.

Abstract: Video at: https://www.math.tamu.edu/conferences/SIAMTXLA/plenary.html A grand challenge in machine learning is the development of computational algorithms that match or outperform humans in perceptual inference tasks that are complicated by nuisance variation. For instance, visual object recognition involves the unknown object position, orientation, and scale, while speech recognition involves the unknown voice pronunciation, pitch, and speed. Recently, a new breed of deep learning algorithms have emerged for high-nuisance inference tasks that routinely yield pattern recognition systems with super-human capabilities. Similar results in language translation, robotics, and games like Chess and Go plus billions of dollars in venture capital have fueled a deep learning bubble and public perception that actual progress is being made towards general artificial intelligence. But fundamental questions remain, such as: Why do deep learning methods work? When do they work? And how can they be fixed when they don't work? Intuitions abound, but a coherent framework for understanding, analyzing, and synthesizing deep learning architectures remains elusive. This talk will discuss the important implications of this lack of understanding for consumers, practitioners, and researchers of machine learning. We will also briefly overview recent progress on answering the above questions based on probabilistic graphs and splines.